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We are looking for an MLOps Engineer to join the Data, AI & ML teams of a leading financial organization.
Direct employment – join the company from day one.
This role combines Machine Learning Engineering, MLOps and Cloud Infrastructure, with a strong focus on building, deploying, operating and continuously improving ML solutions in production, alongside supporting GenAI solutions.
What will you do?
• Design and implement architectures for Machine Learning solutions.
• Lead the deployment of ML models and solutions into production.
• Develop and maintain MLOps and CI/CD pipelines.
• Build and maintain infrastructure that enables Data Science teams to develop, deploy and operate models efficiently and reliably.
• Establish scalable processes for deployment, versioning, monitoring and lifecycle management of ML models.
• Develop and maintain services and APIs supporting Machine Learning solutions.
• Contribute to the development of the organization's ML platform and its evolution.
• Work closely with Data Scientists, Data Engineers, DevOps, Architecture, Cyber Security and Infrastructure teams.
• Support the implementation and operationalization of GenAI solutions.
Requirements
3+ years of experience in MLOps, ML Engineering, or roles involving the deployment and operation of Machine Learning models in production.
Strong development experience with Python and Bash.
Experience working with Docker.
Experience with Git.
Experience developing and maintaining services / APIs supporting ML models.
Hands-on experience with ML deployment, versioning and monitoring processes.
Ability to work effectively with Data Scientists, Data Engineers and DevOps teams.
Strong technical ownership and the ability to lead ML solutions from architecture and design through production deployment.
Why join us?
This is an opportunity to play a key role in building and evolving an enterprise-scale ML platform, working at the intersection of AI, Machine Learning, Cloud and modern software engineering in a complex financial environment.
Nice to have
Experience with CI/CD – GitHub Actions is a significant